Are immortal brains such as ChatGPT LLM the singularity? (Was: The singularity is at the end of the rainbow)

Hi,
Besides interesting discussion of digital immortal
versus analog mortal brains by Geoffrey Hinton .
Also a nice piece of history concerning ChatGPT LLMs.
The key are feature vectors. According to Geoffrey
Hinton’s own statements, there was a prototype of
a Little Language Model (lLM) in 1985,
he mentions it in the middle of his talk here:
Will Digital Intelligence Replace Biological Intelligence?
Geoffrey Hinton - 2024
https://www.youtube.com/watch?v=Es6yuMlyfPw
He spends a few minutes in the talk to explain
how feature vectors can represent meaning of words.
And I suspect his ILM has been reflected in this paper,
probably the ChatGPT LLM ancestor:
Learning Distributed Representations of Concepts
Geoffrey Hinton - 1986
https://www.cs.toronto.edu/~hinton/absps/families.pdf
Prologers should be familier with the example he
uses, i.e. Family Trees. BTW: The family tree of Geoffrey
Hinton himself is also interesting, he is great-great-grandson
of the logician George Boole.
Bye
Mild Shock schrieb:

 How it started:
  > We are the last.
 > The last generation to be unaugmented.
 > The last generation to be intellectually alone.
 > The last generation to be limited by our bodies.
 >
 > We are the first.
 > The first generation to be augmented.
 > The first generation to be intellectually together.
 > The first generation to be limited only by our imaginations.
 How its going:
  > The current discourse around AI and computation seems
 > to be shifting from the singularity (a hypothetical
 > moment when AI surpasses human intelligence in all
 > areas) to breaking computational and conceptual
 > walls—addressing the limits and bottlenecks that
 > arise in computational and cognitive systems.
 >
 > Herbert Simon’s work on bounded rationality
 > acknowledges that human decision-making is constrained
 > by cognitive limits. In AI, we're now grappling with
 > these conceptual walls—AI has its own limits based
 > on algorithms, models, and theoretical understanding
 > of computation.
 >
 > Even with novel algorithms, some fundamental barriers
 > remain due to the intrinsic hardness of certain problems.
 > This could be because of lower bounds on algorithmic
 > complexity or because the problem requires exponential
 > time to solve, regardless of how you design
 > the algorithm.